{
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     "start_time": "2025-05-15T12:49:31.382311Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-15T12:49:32.639668Z",
     "start_time": "2025-05-15T12:49:32.246890Z"
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   },
   "cell_type": "code",
   "source": [
    "features = [\"InvoiceNo\",\"StockCode\",\"Quantity\",\"InvoiceDate\",\"UnitPrice\",\"CustomerID\",\"Country\"]\n",
    "test_csv = pd.read_csv('../CSV/online_retail.csv', encoding='cp1252')\n",
    "test_csv = test_csv[features]\n",
    "print(test_csv[:5])"
   ],
   "id": "31d9dc46f2ff4223",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  InvoiceNo StockCode  Quantity     InvoiceDate  UnitPrice  CustomerID  \\\n",
      "0    536365    85123A         6  12/1/2010 8:26       2.55     17850.0   \n",
      "1    536365     71053         6  12/1/2010 8:26       3.39     17850.0   \n",
      "2    536365    84406B         8  12/1/2010 8:26       2.75     17850.0   \n",
      "3    536365    84029G         6  12/1/2010 8:26       3.39     17850.0   \n",
      "4    536365    84029E         6  12/1/2010 8:26       3.39     17850.0   \n",
      "\n",
      "          Country  \n",
      "0  United Kingdom  \n",
      "1  United Kingdom  \n",
      "2  United Kingdom  \n",
      "3  United Kingdom  \n",
      "4  United Kingdom  \n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-15T12:47:41.257886Z",
     "start_time": "2025-05-15T12:47:41.254332Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "47fe38ee5510733b",
   "outputs": [],
   "execution_count": null
  }
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